When building a data team from scratch or inheriting an existing team, there are plenty of questions to ask when thinking about how to successfully deliver on our mission to the company. Should data engineering be part of the data organization or does it sit better with the engineering team? Data scientist is a job title that means a lot of different things to different companies, what does it mean to us? Are we aligned around platforms or functions? What's our strategy around data governance and compliance? And that's just to name a few.
This talk will present some insights from prior experience on structuring data teams, both at startups and larger legacy organizations, covering examples that have been both successful and not so successful, and lessons learned in each case.
Slides for the presentation given at the Data Science Scotland Meetup (https://www.meetup.com/Scotland-Data-Science-Technology-Meetup/events/256269263/).
This talk aimed to give some general advice, tips, and tricks about how to run a successful data science project.
Hosted by:
Incremental Group - https://www.linkedin.com/company/incremental-group/
MBN Solutions - https://www.linkedin.com/company/mbn-recruitment-solutions/
The Datalab - https://www.linkedin.com/company/the-data-lab-innovation-centre/
Presentation to FourthLion (my former employer) staff on some lessons learnt while doing analytics across three domains, and the motivation for automation. Data science will IMO (a) have significant growing pains (b) see evolution similar to those that we saw in software engineering.
Based on the popular book: Understanding A3 Thinking: A Critical Component of Toyota's PDCA Management System (2008), by Durward K. Sobek II, a synopsis has been presented here.
Medici Technologies common problems with data analysisRiesRobinson
Presentation on data analysis problems for small life science companies. Presentation explains the problems and how Medici Technologies fills this need. Medici accelerates client progress toward goals using a structured approach that incorporates project management, parallel algorithmic assessment, optimization, robustness testing, and validation.
Slides for the presentation given at the Data Science Scotland Meetup (https://www.meetup.com/Scotland-Data-Science-Technology-Meetup/events/256269263/).
This talk aimed to give some general advice, tips, and tricks about how to run a successful data science project.
Hosted by:
Incremental Group - https://www.linkedin.com/company/incremental-group/
MBN Solutions - https://www.linkedin.com/company/mbn-recruitment-solutions/
The Datalab - https://www.linkedin.com/company/the-data-lab-innovation-centre/
Presentation to FourthLion (my former employer) staff on some lessons learnt while doing analytics across three domains, and the motivation for automation. Data science will IMO (a) have significant growing pains (b) see evolution similar to those that we saw in software engineering.
Based on the popular book: Understanding A3 Thinking: A Critical Component of Toyota's PDCA Management System (2008), by Durward K. Sobek II, a synopsis has been presented here.
Medici Technologies common problems with data analysisRiesRobinson
Presentation on data analysis problems for small life science companies. Presentation explains the problems and how Medici Technologies fills this need. Medici accelerates client progress toward goals using a structured approach that incorporates project management, parallel algorithmic assessment, optimization, robustness testing, and validation.
Advances in technology for capturing information have led to the promise of “Big Data” to dramatically alter the business environment. However, technology is only an enabler of aggregation and analysis. Many firms struggle to convert information to business knowledge and insights. Learn how organizations are using data to improve skill development at all levels and developing models for organizational structures to link these skills to executive decision-making.
Speakers: Dan McGurrin, Ph.D., NC State and Pamela Webber, Cisco
Introduction to Business Analytics Part 1 published by BeamSync.
BeamSync is providing business analytics training course in Bangalore. If you are looking for analytics training then visit BeamSync. Regular classes are running during the weekend.
For details visit: http://beamsync.com/business-analytics-training-bangalore/
Speaker: Venkatesh Umaashankar
LinkedIn: https://www.linkedin.com/in/venkateshumaashankar/
What will be discussed?
What is Data Science?
Types of data scientists
What makes a Data Science Team? Who are its members?
Why does a DS team need Full Stack Developer?
Who should lead the DS Team
Building a Data Science team in a Startup Vs Enterprise
Case studies on:
Evolution Of Airbnb’s DS Team
How Facebook on-boards DS team and trains them
Apple’s Acqui-hiring Strategy to build DS team
Spotify -‘Center of Excellence’ Model
Who should attend?
Managers
Technical Leaders who want to get started with Data Science
The Perfect Storm: SAP, StarRez & Enterprise Data IntegrationSalesforce.org
Presentation from Salesforce.org Higher Ed Summit 2018 by: Adam Recktenwald, Enterprise Applications, and Todd Bran, Analytics Assessment and Decision Support.
SAP is the the ERP system of record for the University of Kentucky. It's where our applicant records and currently enrolled students’ course information, grades, and additional points of data live. The SAP Integration team at the University of Kentucky went through an extensive evaluation and auditing process for integrating student data based on the HEDA data model. In this session, see how the team integrated and validated applicant, admitted and confirmed and housing data, and shaped the way student statuses were utilized within the Program Enrollment object. The team will share the process for daily extract, transform and load (ETL) processes across several data sources across the University. Discussions about additional data integrations important to the university will be discussed.
Watch a recording of this presentation: https://youtu.be/ibnQZx-usa8
Start With Why: Build Product Progress with a Strong Data CultureAggregage
Have you ever thought your product's progress was headed in one direction, and been shocked to see a different story reflected in big picture KPIs like revenue? It can be confusing when customer feedback or metrics like registration or retention are painting a different picture. No matter how sophisticated your analytics are, if you're asking the wrong questions - or looking at the wrong metrics - you're going to have trouble getting answers that can help you.
Join Nima Gardideh, CTO of Pearmill, as he demonstrates how to build a strong data culture within your team, so everyone understands which metrics they should actually focus on - and why. Then, he'll explain how you can use your analytics to regularly review progress and successes. Finally, he'll discuss what you should keep in mind when instrumenting your analytics.
Start With Why: Build Product Progress with a Strong Data CultureBrittanyShear
Have you ever thought your product's progress was headed in one direction, and been shocked to see a different story reflected in big picture KPIs like revenue? It can be confusing when customer feedback or metrics like registration or retention are painting a different picture. No matter how sophisticated your analytics are, if you're asking the wrong questions - or looking at the wrong metrics - you're going to have trouble getting answers that can help you.
Join Nima Gardideh, CTO of Pearmill, as he demonstrates how to build a strong data culture within your team, so everyone understands which metrics they should actually focus on - and why. Then, he'll explain how you can use your analytics to regularly review progress and successes. Finally, he'll discuss what you should keep in mind when instrumenting your analytics.
Methods of Organizational Change ManagementDATAVERSITY
The disparity between expecting change and managing it — the “change gap” — is growing at an unprecedented pace. This has put many information management shops into traction as they initiate large, complex projects needed to stay competitive.
Information management professionals and business leaders must concern themselves with the organization’s acceptance of these efforts. To be successful in achieving the larger enterprise goals, these initiatives must transform the organization. However, it takes more than wishful thinking to bridge the gap.
The complexities of engaging behavioral and enterprise transformation are too often underestimated at great peril because the “soft stuff” is truly hard.
Business leaders everywhere are looking to data to inform their decision making. Accompanying this demand are misunderstandings of what it takes to transform data into something that can inform a decision. What is the data infrastructure required? In this talk, I'll dispel some of these misunderstandings and discuss what it takes to build good data infrastructure. I'll discuss the components of a good data infrastructure. The best practices and available tools for gathering data, processing it, storing it, analyzing it and communicating the results. The goal is for these components to create a data infrastructure which can evolve from simple reporting to sophisticated insights for decision making.
Presented at OpenWest 2018
Why And How to Transition into Product Management by Google PMProduct School
Nabil Shahid walks through their journey to Product Management in the world of tech, talking about how to market your skills and how to get into the industry. He also touches on balancing knowledge and personal experience with what's best for a wider user group.
Advances in technology for capturing information have led to the promise of “Big Data” to dramatically alter the business environment. However, technology is only an enabler of aggregation and analysis. Many firms struggle to convert information to business knowledge and insights. Learn how organizations are using data to improve skill development at all levels and developing models for organizational structures to link these skills to executive decision-making.
Speakers: Dan McGurrin, Ph.D., NC State and Pamela Webber, Cisco
Introduction to Business Analytics Part 1 published by BeamSync.
BeamSync is providing business analytics training course in Bangalore. If you are looking for analytics training then visit BeamSync. Regular classes are running during the weekend.
For details visit: http://beamsync.com/business-analytics-training-bangalore/
Speaker: Venkatesh Umaashankar
LinkedIn: https://www.linkedin.com/in/venkateshumaashankar/
What will be discussed?
What is Data Science?
Types of data scientists
What makes a Data Science Team? Who are its members?
Why does a DS team need Full Stack Developer?
Who should lead the DS Team
Building a Data Science team in a Startup Vs Enterprise
Case studies on:
Evolution Of Airbnb’s DS Team
How Facebook on-boards DS team and trains them
Apple’s Acqui-hiring Strategy to build DS team
Spotify -‘Center of Excellence’ Model
Who should attend?
Managers
Technical Leaders who want to get started with Data Science
The Perfect Storm: SAP, StarRez & Enterprise Data IntegrationSalesforce.org
Presentation from Salesforce.org Higher Ed Summit 2018 by: Adam Recktenwald, Enterprise Applications, and Todd Bran, Analytics Assessment and Decision Support.
SAP is the the ERP system of record for the University of Kentucky. It's where our applicant records and currently enrolled students’ course information, grades, and additional points of data live. The SAP Integration team at the University of Kentucky went through an extensive evaluation and auditing process for integrating student data based on the HEDA data model. In this session, see how the team integrated and validated applicant, admitted and confirmed and housing data, and shaped the way student statuses were utilized within the Program Enrollment object. The team will share the process for daily extract, transform and load (ETL) processes across several data sources across the University. Discussions about additional data integrations important to the university will be discussed.
Watch a recording of this presentation: https://youtu.be/ibnQZx-usa8
Start With Why: Build Product Progress with a Strong Data CultureAggregage
Have you ever thought your product's progress was headed in one direction, and been shocked to see a different story reflected in big picture KPIs like revenue? It can be confusing when customer feedback or metrics like registration or retention are painting a different picture. No matter how sophisticated your analytics are, if you're asking the wrong questions - or looking at the wrong metrics - you're going to have trouble getting answers that can help you.
Join Nima Gardideh, CTO of Pearmill, as he demonstrates how to build a strong data culture within your team, so everyone understands which metrics they should actually focus on - and why. Then, he'll explain how you can use your analytics to regularly review progress and successes. Finally, he'll discuss what you should keep in mind when instrumenting your analytics.
Start With Why: Build Product Progress with a Strong Data CultureBrittanyShear
Have you ever thought your product's progress was headed in one direction, and been shocked to see a different story reflected in big picture KPIs like revenue? It can be confusing when customer feedback or metrics like registration or retention are painting a different picture. No matter how sophisticated your analytics are, if you're asking the wrong questions - or looking at the wrong metrics - you're going to have trouble getting answers that can help you.
Join Nima Gardideh, CTO of Pearmill, as he demonstrates how to build a strong data culture within your team, so everyone understands which metrics they should actually focus on - and why. Then, he'll explain how you can use your analytics to regularly review progress and successes. Finally, he'll discuss what you should keep in mind when instrumenting your analytics.
Methods of Organizational Change ManagementDATAVERSITY
The disparity between expecting change and managing it — the “change gap” — is growing at an unprecedented pace. This has put many information management shops into traction as they initiate large, complex projects needed to stay competitive.
Information management professionals and business leaders must concern themselves with the organization’s acceptance of these efforts. To be successful in achieving the larger enterprise goals, these initiatives must transform the organization. However, it takes more than wishful thinking to bridge the gap.
The complexities of engaging behavioral and enterprise transformation are too often underestimated at great peril because the “soft stuff” is truly hard.
Business leaders everywhere are looking to data to inform their decision making. Accompanying this demand are misunderstandings of what it takes to transform data into something that can inform a decision. What is the data infrastructure required? In this talk, I'll dispel some of these misunderstandings and discuss what it takes to build good data infrastructure. I'll discuss the components of a good data infrastructure. The best practices and available tools for gathering data, processing it, storing it, analyzing it and communicating the results. The goal is for these components to create a data infrastructure which can evolve from simple reporting to sophisticated insights for decision making.
Presented at OpenWest 2018
Why And How to Transition into Product Management by Google PMProduct School
Nabil Shahid walks through their journey to Product Management in the world of tech, talking about how to market your skills and how to get into the industry. He also touches on balancing knowledge and personal experience with what's best for a wider user group.
The environment for software development is changing so quickly that it's challenging for companies to stay afloat. In this highly competitive environment, having a good IT testing strategy in place is crucial. In such a case, hiring an appropriate test data manager is the key to success. This article gives you insights into hiring one. Check it out!
Building digital product masters to prevail in the age of accelerations parts...Jeffrey Stewart
Straight talk on an essential element in your risk mitigation and your organizations success
This three part story will show how building a Digital Product Master (DPM) mitigates unfunded liability risks and enables organizations to move fast, adapt quickly, and improve the top line revenue.
It will answer a number of questions...
Why should we begin building a DPM?
Why is it important for B2B SaaS firms and connected IoT product owners?
What is a Digital Product Master / what is it NOT?
Who is accountable for defining their firm’s DPM strategy?
What are unfunded liabilities and risks in the current age?
Where are we headed that make DPMs to crucial?
Each of the three parts are inspired by real life stories. The stories help frame the lessons learned that are the rationale for Building a Digital Product Master for your organization.
Part 1 of 3: Prevailing in the Age of Acceleration
Unfunded Liabilities - Technical Debt, Change Shocks,Capital Efficiency
Part 2 of 3: Meet a Digital Product Master
Business Enterprise Architecture Right-Sized for your Protection
Part 3 of 3: A Case to Study
Digital Roadmap for Teams, Tools, and Flows with four framework models
Are you ready for Data science? A 12 point testBertil Hatt
Presentation for the MancML on data readiness.
If you are considering starting to invest in Data science, this is a helpful guide to understand:
- what you need *before* you start looking for a Data scientist
- the skillset and experience that you should be looking for when you do.
Measuring the Productivity of Your Engineering Organisation - the Good, the B...Marin Dimitrov
High-performing engineering teams regularly dedicate time on measuring the performance & quality of the systems and applications they’re building or on measuring & improving the various aspects of the development lifecycle. High-performing product companies are also data-driven when it comes to measuring the impact of new features & products in terms of business KPIs and Northstar metrics.
Can a data-driven approach be applied to measuring the performance, maturity and continuous improvement of an engineering team or the whole engineering organisation? In this discussion we’ll cover various important topics related to quantifying the performance of an engineering organisation
UX, DX, DSX: Developers and Data Scientists as UsersUXDXConf
More and more companies nowadays are investing heavily in building infrastructure for developers and data scientists. But often, building infrastructure products are treated as pure engineering practices and differentiated from feature products.
I would like to share my experience leading a team at BuzzFeed in building user-centric infrastructure products for our developers and data scientists, and how I integrate and adapt traditional PM techniques for technical products.
Building software for our peers is a double-edged sword. On one hand, our users are technologists themselves and have immense appreciation for well-designed infrastructure and tools. On the other hand, it is very tempting for us as developers to make assumptions about those folks with whom we work closely. When building tools for data scientists, it is especially crucial to keep in mind that they have their own distinct workflows and needs.
This document is containing details about Business Analysis & Business Analyst the agendas are as below :
Introduction to Business Analysis
Scope of Business Analyst in IT & Non-IT Organizations
Require Skill Matrix & Prerequisites for Business Analyst
Business Analysis Methodology
Role Business Analyst in SDLC
Alternatives & BA Professional Courses
Introduction to CMMi Levels & Role of BA in CMMi Levels
Data Science. Business Analytics is the statistical study of business data to gain insights. Data science is the study of data using statistics, algorithms and technology. Uses mostly structured data. Uses both structured and unstructured data.
Data Science is in high demand, the melting pot
of complex skills requires a qualified data scientist have made them the unicorns in today's data-driven landscape.
Big data is a term that describes the huge amount of data (structured and unstructured) that floods the enterprise every day.
Big Data includes the quantity of data , the speed or speed at which it's created and picked up , and therefore the variety or scope of the info points being covered. It very often comes from several sources and arrives in multiple formats.
From the perspective of a project manager or project manager, big data does not necessarily revolve around the amount of data that individuals and companies deal with. Data can be obtained from any source and analyzed to find the answer for the following purposes:
Reduce the time cut costs
Wise decision
Optimized product
New products development
Your present project management and soft skills are likely ultimate for establishing the framework for a replacement or existing Big Data project team and their projects. you only got to enhance the talents and knowledge you have already got .
This is where Tonex training can help.
Tonex Offers Big Data for Project and Program Managers Training
participants will find out how to profit from big data in their projects and programs
Why does one Need This Training?
Need project managers with big data expertise and business awareness
Must have expert judgment ability to use technology
The plan manager should assist in expanding and coordinating tasks throughout the project
Audience
Project managers
Program managers
Big data analytics
Decision makers of organizations
Strategic leaders
Executives
Training Objectives
Describe the big data analytics
Explain the business values of massive data
Talk about the opportunities and challenges of using big data
Choose if big data analytics serve their client’s interest, situation and knowledge
Manage data analytic projects
Assess risks related to the large data
Distinguish between a knowledge analytic project and a fishing expedition
Decide the best approach
Conclude the time to stop the analysis
Talk about how project management can be used to sustain your data analytics capability
Elaborate how big data can be used to secure the progress of the project
Identify what analytics should be implemented
Course Outline:
Overview to Big Data and Project/Program Management
Project Management Process
Where Does Big Data Analytics expertise is Required?
Introduction to Big Data Management
Big Data Challenges
The Status of Big Data Management
Data Science Methods
Technical Practices for Big Data Management
Analytic Exercises and Big Data Management
Applicable Programming Languages
Corporation Practices for Big Data Management
Top Priorities of Big Data Management
Choosing the Best Strategy
Organizational Leadership
Tonex Hands-On Sample Workshop
Learn More:
https://www.tonex.com/training-courses/big-data-project-program-managers-training/
Big data jobs are taking the highest rankings in the job market. Learn how you can excel in big data job roles as analysts, scientists, or engineers here.
Keynote Evento TestingUY 2018 - The Art of Excellence Adding value as an IT p...TestingUy
Expositor: Derk-Jan De Grood
Resumen: In order to distinguish themselves and meet customer expectations organizations need to embrace change. In his keynote Derk-Jan de Grood will explain how Continuous Delivery, DevOps and Scaling Agile aim to effectively react to disruptive innovations, but introduce new challenges. Organization have a need for Visionary’s, Explorers and Experts to make the transition. Develop yourself and your team in order to keep adding value and embrace the new opportunities that arise.
Data Science Popup Austin: Conflict in Growing Data Science Organizations Domino Data Lab
Watch talk ➟ http://bit.ly/1NKPpQh
Eduardo Arino De La Rubia, VP of Product and Data Scientist in residence at Domino Data Lab talks about how to manage conflict in growing data science teams.
A lot of companies make the mistake of thinking that just hiring Data Scientists will lead to increased revenue or increased profit. For a company’s investment in Data Science to be successful the Data Scientists need to work on the right problems, with the right people, and with the right tools. In this presentation, I will talk about the lessons I have learned, and mistakes made in applying Data Science in commercial settings over the last 10 years. I will highlight what processes can increase the chances of Data Science investment being successful.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
2. Why do I like talking about building
data teams?
I’ve done it a lot!
I’m currently on round 3
● Round 1 Timeline Labs - Seed Stage start-up
● Round 2 Tribune Publishing / tronc - Large legacy organization
● Round 3 Los Angeles Times - Slightly smaller legacy organization
Every organization is different. What works at one company may need to be modified for
another.
3. What makes any organization
successful?
● Performance Deliver projects on time that meet the needs of the business
● Agility Easily adjust team goals and work tasks to meet changing business needs
● Communication Intra- and inter- team communication are critical to any complex
project’s success
● Collaboration Successful teams don’t work in a bubble, they work hand in hand with
many different elements of the business
4. Data orgs often face unique challenges
compared to other technical teams
● Analytics groups can easily get stuck in a service request mode rather than being
product focused
● Machine learning groups are often working from business requirements, but the exact
solution is generally not understood until some research and prototyping is done
● Frameworks for deploying data and ML solutions haven’t standardized, solutions are
often one-off even inside a single company or team and certainly vary across
companies
5. What might hinder us from reaching
our goals?
There are always restrictions placed on us that limit what
we can do to achieve our goals
● Budgets and Headcount
● Proving ROI (particularly when it comes to new ML
groups)
● Existing structures and processes
6. How we are building our data org at the
LA Times
7. Why have data engineering and data
science in the same org?
Benefits of bringing together DE and DS
● Improve data scientist developer chops
● Increase alignment between engineering and data science
● Reduce challenges in getting machine learning models into
production
● Increase ownership of engineering guidance to data science
Case Study: Failures in delivering meaningful ML models at TLL
Challenges
● Timeline to develop models too long compared to product
needs
● Platform Engineering and Data Science in different orgs
8. Consumer Research?
Case Study: New
approach at Los
Angeles Times
What does a standard
big data approach tell us
from this chart?
9. Consumer Research
Case Study: New
approach at Los
Angeles Times
Consumer research -
what is going on in the
bottom left?
10. Putting a more rigorous process around
data science
Descriptive Analytics
Decision Science
Machine Learning
Explore your data. Find some ideas. Gain inspiration. CANNOT BE
USED FOR BUSINESS DECISIONS
Take the ideas found above, and validate that they are actually true
Automate the ideas that were validated to drive true business value
Cassie Kozyrkov - Chief Decision Scientist at Google
11. Not all data scientists are the same
Data Science is a pretty vague term
Is the Type A - Analysis and Type B - Building view of the world* enough?
*Michael Hochster (Stitch Fix) and Robert Chang (AirBNB)
Machine Learning
Engineer
Decision ScientistDescriptive Analyst
12. How do we take action on different
types of data scientists?
Machine Learning Engineer
Descriptive Analyst Descriptive Analyst
13. How do we take action on different
types of data scientists?
We don’t have any Decision Scientists
● Overall org isn’t big enough
● Work is not full-time
Tribune Publishing - Made Data Science team the official testing team
● Majority of A/B testing was done by marketing group
● Found many key issues with more complicated scenarios
○ Missing clear hypotheses
○ Test structure sometimes incorrect (too many changing variables)
● Created test plan document, data science had to work with marketing to come to
agreement on plan that got data science stamp of approval
Changes to hiring practices
● Distinct JDs for each role
● Different skill sets for Talent Acquisition to look for
14. What do these candidates look like at
LAT?
Machine Learning Engineer Descriptive Analyst
● Generally STEM background
● Experience with ML in production systems
● Stronger coding skills
● Has to know Python (or whatever DE is
using)
● Desire to learn more developer skills
● Broader backgrounds (MBA, econ, DS
training programs)
● R or Python
● Skill set emphasis
○ Data interpretation
○ Communication and presentation
○ Data visualization
15. Data Governance and Compliance
VPPA, GDPR, CCPA
Where should this role sit? Legal? Compliance? Security?
Data?
Why I like the data org:
● Focus on data documentation (lineage, dictionaries, etc.)
● Increases awareness
○ For rest of the org: reinforces they need to think about this
in their daily job
○ For Compliance / Governance: more rapid discovery of
what work is being done and injecting suggestions into the
development process earlier